A Distance for HMMs Based on Aggregated Wasserstein Metric and State Registration

Yukun Chen; Jianbo Ye; Jia Li

doi:10.1007/978-3-319-46466-4_27

ScienceGate Book Chapters

BOOK-CHAPTER

A Distance for HMMs Based on Aggregated Wasserstein Metric and State Registration

Yukun Chen Jianbo Ye Jia Li

Year: 2016 Lecture notes in computer science Pages: 451-466 Publisher: Springer Science+Business Media

DOI: 10.1007/978-3-319-46466-4_27

Get Full-Text PDF Get Analytical Report

Keywords:

Mixture model Hidden Markov model Wasserstein metric Kullback–Leibler divergence Metric (unit) Divergence (linguistics) Gaussian Pattern recognition (psychology) Mathematics Computer science Marginal distribution Artificial intelligence Algorithm Applied mathematics Random variable Statistics

Metrics

Cited By

1.34

FWCI (Field Weighted Citation Impact)

Refs

0.82

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Anomaly Detection Techniques and Applications

Physical Sciences → Computer Science → Artificial Intelligence

Time Series Analysis and Forecasting

Physical Sciences → Computer Science → Signal Processing

Gaussian Processes and Bayesian Inference

Physical Sciences → Computer Science → Artificial Intelligence

A Distance for HMMs Based on Aggregated Wasserstein Metric and State Registration

Metrics

Citation History

Topics

Related Documents

Aggregated Wasserstein Distance and State Registration for Hidden Markov Models

Saliency detection based on aggregated Wasserstein distance

Wasserstein distance and metric trees

Wasserstein distance and metric trees

A New Interval Data Distance Based on the Wasserstein Metric